Σύγκριση μεθόδων

	Ανάλυση Εμπλουτισμού Μονοπατιών Βάσει Δικτύου	Ανάλυση Εμπλουτισμού Γονιδιακών Συνόλων (GSEA)
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2002 (seminal network-scoring concept); matured 2010–2015	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Δημιουργός	Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Τύπος	Pathway enrichment and network analysis method	Functional genomics / enrichment analysis
Θεμελιώδης πηγή	Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl_1), S233–S240.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
Εναλλακτικές ονομασίες	network pathway enrichment, network-based enrichment, topology-based pathway analysis, NBPEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Συναφείς	1	5
Σύνοψη	Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enrichment approaches that treat pathway genes as independent lists, this family of methods propagates signals across network edges, capturing the topology of interactions and uncovering dysregulated modules that flat-list enrichment would miss.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.